Use of Landsat Thermal‐IR Data and GIS in Soil Moisture Assessment

Shih, S. F.; Jordan, Jonathan D.

doi:10.1061/(asce)0733-9437(1993)119:5(868)

Cited by 17 publications

(8 citation statements)

References 14 publications

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“…Soil spectroscopy in the spectral resolution of satellite sensors has been used to estimate volumetric soil water content. For instance, Shih and Jordan (1993) reported that the reflectance values at wavelengths between 1450 and 2350 nm are inversely related to the soil water content. They suggested that Landsat Thematic Mapper Bands 5 and 7 can be used to estimate soil water content using satellite remote sensing.…”

mentioning

confidence: 99%

Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

Babaeian

Homaee

Montzka

et al. 2015

Vadose Zone Journal

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In this study, we developed spectrotransfer functions (STFs) that relate soil hydraulic properties (SHPs) to spectral relectance values to estimate hydraulic parameters of the Mualem -van Genuchten (MvG) model. We investigated the general potential of airborne as well as space-borne remote sensors to retrieve MvG hydraulic parameters of a bare soil agricultural ield. Based on the ASD full spectrum (Scenario I), simple spectral signatures were generated mimicking the hyperspectral EnMAP sensor (Scenario II), and the multispectral Sentinel-2 sensor (Scenario III). A stepwise multiple linear regression method was used for each scenario to derive STFs. We further tested laboratory-and soil-map-based HYPRES and Rosetta pedotransfer functions (PTFs) to parameterize MvG parameters and thus provide soil water characteristics and hydraulic conductivity functions in the region. The best results were obtained for Scenarios I and II, with similar R 2 values for shape parameters a* and n and the lognormal saturated hydraulic conductivity (K s *). The R 2 values were highest for K s * in Scenarios I and II (0.58 and 0.57, respectively). The R 2 values for a* and n were 0.30 and 0.34 in Scenario I and 0.39 and 0.31 in Scenario II, respectively. In all scenarios, the lowest R 2 values were obtained for saturated water content (q s ), with values around 0.10 for Scenarios I and II and almost zero in Scenario III. Compared with HYPRES and Rosetta PTFs, the spectral approach performed reasonably well in terms of predicting soil water retention characteristics and unsaturated hydraulic conductivity. These indings suggest that spectral relectance data provide a promising indirect and quick method for large-scale parameter estimation.

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confidence: 99%

Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

Babaeian

Homaee

Montzka

et al. 2015

Vadose Zone Journal

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“…Remote sensing methods such as electromagnetic induction (DeJong et al, 1979;Rhoades & Corwin, 1981;Williams & Baker, 1982;Oluic & Kovacevic, 1983;Wollenhaupt et al, 1986;Palacky, 1987;Williams & Hoey, 1987;Kachanoski et al, 1988;Mazac et al, 1988;Corwin & Rhoades, 1990;Slavich & Petterson, 1990;Diaz & Herrero, 1992;Greenhouse & Slaine, 1983;Sudduth & Kitchen, 1993;Doolittle et al, 1994;Jaynes et al, 1995;Lesch et al, 1995) electrical resistivity tomography (Mazac et al, 1988;Daily et al, 1992), near-IR measurements (Sudduth & Hummel, 1993),x-ray tomography (Tollner, 1994) thermal-IR measurements (Ottle et al, 1989;Jupp et al, 1990;Shih & Jordan, 1993;Moran et al, 1994b), NOAA advanced very high resolution radiometry (Huang et al, 1995) microwave measurements (Jackson & Schmugge, 1986;Jupp et al, 1990;Wood et al, 1993;Schmugge et al, 1994) ground penetrating radar (Topp et al, 1980;Doolittle, 1987;Truman et al, 1988;Raper et al, 1990;Kung & Donohue, 1991;Kung & Lu, 1993), and multispectral scanning (Everitt et al, 1977;Chaturvedi et al...…”

Section: Measurement and Estimation Methodsmentioning

confidence: 99%

“…Satellite data can be used to improve the definition of soils and land covers that are needed to determine regional distributions of infiltration, ET and runoff coefficients. The most important remotely sensed satellite information for surface and subsurface hydrologists is probably the estimation of soil moisture and ET derived from satellite thermalinfrared images and/or NOAA advanced very high resolution radiometer (AVHRR) satellite data used in combination with energy balance models at the land-atmosphere interface (Carlson, 1985;Engman, 1986;Taconet et al, 1986;Ottle et al, 1989;Carlson et al, 1990;Shih & Jordan, 1993;Moran et al, 1994b;Huang et al, 1995) and the determination of effective meso-scale hydraulic properties using the inverse modeling approach combined with remotely sensed data from surface reflectance, surface temperature, and multifrequency microwave techniques (Feddes et al, 1993). Microwave techniques, particularly passive microwave measurements, have shown good correlation with ground data of surface soil moisture (Jackson & Schmugge, 1986;Wood et al, 1993;Chaturvedi et al, 1983;Schmugge et al, 1994).…”

Section: Measurement and Estimation Methodsmentioning

confidence: 99%

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Corwin

2015

SSSA Special Publications

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In recent years, worldwide attention has shifted from point source to non-point source (NPS) pollutants, particularly with regard to the pollution of surface and subsurface sources of drinking water. This is due to the widespread occurrence and potential chronic health effects of NPS pollutants. The ubiquitous nature of NPS pollutants poses a complex technical problem. The area1 extent of their contamination increases the complexity and sheer volume of data required for assessment far beyond that of typical point source pollutants. The spatial nature of the NPS pollution problem necessitates the use of a geographic information system (GIS) to manipulate, retrieve, and display the large volumes of spatial data. This chapter provides an overview of the components (i.e., spatial variability, scale dependency, parameter-data estimation and measurement, uncertainty analysis, and others) required to successfully model NPS pollutants with GIS and a review of recent applications of GIS to the modeling of non-point source pollutants in the vadose zone with deterministic solute transport models. The compatibility, strengths, and weaknesses of coupling a GIS to deterministic one-dimensional transport models are discussed. BACKGROUND IN NON-POINT SOURCE POLLUTANTS

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“…A soil with a high thermal inertia (due to a high soil moisture content) will have a lower diurnal range of surface temperature (Schmugge et al, 1980). Shih and Jordan (1993) indicated that integration of the lands surface temperature data from the Landsat TM TIR imagery (band 6) with GIS can provide useful information for periodic (8 to 16 days) monitoring of the spatial distribution of soil moisture conditions. They summarized three important issues.…”

Section: Thermal Infrared Methodsmentioning

confidence: 99%

Integration of Remote Sensing and GIS for Hydrologic Studies

Shih¹

1996

Geographical Information Systems in Hydrology

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Abstract. Recently, the importance of integration of remote sensing and geographic information system (GIS) to study hydrologic processes has been realized by water-resources workers. However, the interface between remotely sensed data and GIS is still weak and many problems must be solved before it becomes widely available. In the meantime, the public use of satellite data to manage water resources is still in its infancy, and more application techniques are urgently in need of development. Thus this chapter is separated into two major parts. The first part is to introduce general information about remote sensing systems, GIS, and the global positioning system (GPS). The second part is to exemplify successful applications of the integration of remote sensing and GIS in hydrologic studies such as land use/land cover classification, precipitation, soil moisture, evapotranspiration, water extent, groundwater, water quality, and runoff. IntroductionThe endless recirculation of water in the atmosphere-hydrosphere-lithosphere is known as the hydrologic cycle. The cycle can be studied according to the particular scale of reference i.e., global scale, basin scale, etc. From the hydrological point of view, the basin scale cycle is emphasized. The basin-scale cycle is considered to be a continuous circulation of water from water vapor to precipitation, stream flow, lakes, reservoirs, soil moisture, groundwater, and out of basin transfer, to the return to water vapor through evaporation and transpiration. Within a basin, the dynamics of the hydrologic processes are governed partially by the temporal and spatial characteristics of inputs and outputs and the land use/land cover conditions. Currently, little is known about the accuracy of the estimation of hydrologic parameters in a given area, primarily because of the wide variation of the distribution in

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Use of Landsat Thermal‐IR Data and GIS in Soil Moisture Assessment

Cited by 17 publications

References 14 publications

Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

Towards Retrieving Soil Hydraulic Properties by Hyperspectral Remote Sensing

GIS Applications of Deterministic Solute Transport Models for Regional-Scale Assessment of Non-Point Source Pollutants in the Vadose Zone

Integration of Remote Sensing and GIS for Hydrologic Studies

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